为解决多应力条件下加速寿命试验中建立复合加速模型困难、模型参数难以求解以及建模过程中通常忽 略应力间耦合作用的问题，根据天牛须搜索建立改进的BP 神经网络模型。使用多应力加速寿命试验中收集的4 组 应力水平的失效数据对BAS-BP 神经网络模型进行训练，对第5 组应力水平下的可靠度与失效时间进行预测。利用 平均相对误差、拟合优度2 个参数对模型的预测结果进行评价，并与BP 神经网络的预测结果进行对比。结果表明， BAS-BP 神经网络具有更好的准确性及鲁棒性。
In order to solve the problems of establishing complex acceleration model in accelerated life test under multi-stress conditions, the model parameters are difficult to solve and the stress coupling problem is usually neglected in the modeling process, an improved BP neural network model is established according to beetle antennae search (BAS). The BAS-BP neural network model was trained using the failure data of the 4 sets of stress levels collected in the multi-stress accelerated life test to predict the reliability and failure time of the 5th set of stress levels. The prediction results of the model were evaluated by using the average relative error and the goodness of fit, and compared with the prediction results of BP neural network. The results show that the BAS-BP neural network has better accuracy and robustness.
葛 峰.基于BAS-BP 神经网络的多应力加速寿命试验预测方法[J].,2020,39(06).复制